Classification of Marine Vessels in a Littoral Environment Using a Novel Training Database

Research into object classification has led to the creation of hundreds of databases for use as training sets in object classification algorithms. Datasets made up of thousands of cars, people, boats, faces and everyday objects exist for general classification techniques. However, no commercially available database exists for use with detailed classification and categorization of marine vessels commonly found in littoral environments. This research seeks to fill this void and is the combination of a multi-stage research endeavor designed to provide the missing marine vessel ontology. The first of the two stages performed to date introduces a novel training database called the Lister Littoral Database 900 (LLD-900) made up of over 900 high-quality images. These images consist of high-resolution color photos of marine vessels in working, active conditions taken directly from the field and edited for best possible use. Segmentation masks of each boat have been developed to separate the image into foreground and background sections. Segmentation masks that include boat wakes as part of the foreground section are the final image type included. These are included to allow for wake affordance detection algorithms rely on the small changes found in wakes made by different moving vessels. Each of these three types of images are split into their respective general classification folders, which consist of a differing number of boat categories dependent on the research stage. In the first stage of research, the initial database is tested using a simple, readily available classification algorithm known as the Nearest Neighbor Classifier. The accuracy of the database as a training set is tested and recorded and potential improvements are documented. The second stage incorporates these identified improvements and reconfigures the database before retesting the modifications using the same Nearest Neighbor Classifier along with two new methods known as the K-Nearest Neighbor Classifier and the Min-Mean Distance Classifier. These additional algorithms are also readily available and offer basic classification testing using different classification techniques. Improvements in accuracy are calculated and recorded. Finally, further improvements for a possible third iteration are discussed. The goal of this research is to establish the basis for a training database to be used with classification algorithms to increase the security of ports, harbors, shipping channels and bays. The purpose of the database is to train existing and newly created algorithms to properly identify and classify all boats found in littoral areas so that anomalous behavior detection techniques can be applied to determine when a threat is present. This research represents the completion of the initial steps in accomplishing this goal delivering a novel framework for use with littoral area marine vessel classification. The completed work is divided and presented in two separate papers written specifically for submission to and publication at appropriate conferences. When fully integrated with computer vision techniques, the database methodology and ideas presented in this thesis research will help to provide a vital new level of security in the littoral areas around the world

Transductive hyperspectral image classification: toward integrating spectral and relational features via an iterative ensemble system

Remotely sensed hyperspectral image classification is a very challenging task due to the spatial correlation of the spectral signature and the high cost of true sample labeling. In light of this, the collective inference paradigm allows us to manage the spatial correlation between spectral responses of neighboring pixels, as interacting pixels are labeled simultaneously. The transductive inference paradigm allows us to reduce the inference error for the given set of unlabeled data, as sparsely labeled pixels are learned by accounting for both labeled and unlabeled information. In this paper, both these paradigms contribute to the definition of a spectral-relational classification methodology for imagery data. We propose a novel algorithm to assign a class to each pixel of a sparsely labeled hyperspectral image. It integrates the spectral information and the spatial correlation through an ensemble system. For every pixel of a hyperspectral image, spatial neighborhoods are constructed and used to build application-specific relational features. Classification is performed with an ensemble comprising a classifier learned by considering the available spectral information (associated with the pixel) and the classifiers learned by considering the extracted spatio-relational information (associated with the spatial neighborhoods). The more reliable labels predicted by the ensemble are fed back to the labeled part of the image. Experimental results highlight the importance of the spectral-relational strategy for the accurate transductive classification of hyperspectral images and they validate the proposed algorithm